Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualiz...Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.展开更多
Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best asp...Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.展开更多
Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communi...Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.展开更多
针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提...针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。展开更多
在局部遮荫下,针对传统最大功率跟踪MPPT(maximum power point tracking)算法不能跳出局部最优找到全局最大功率,及传统蝴蝶优化算法BOA(butterfly optimization algorithm)存在搜索震荡大和收敛慢等问题,提出一种新型的MPPT控制算法。...在局部遮荫下,针对传统最大功率跟踪MPPT(maximum power point tracking)算法不能跳出局部最优找到全局最大功率,及传统蝴蝶优化算法BOA(butterfly optimization algorithm)存在搜索震荡大和收敛慢等问题,提出一种新型的MPPT控制算法。该算法在传统蝴蝶算法上加入收敛因子,来加快全局搜索速度;引入自适应权重系数,来提高蝴蝶优化算法在局部搜索的搜索速度及追踪精度等性能。通过仿真,对比混合算法(INBOA)与BOA、粒子群优化PSO(particle swarm optimization)算法、灰狼优化算法GWO(gray wolf optimization)的函数收敛曲线,验证所提算法具有收敛速度快、搜索精度高的优点;对比INBOA、BOA、PSO、GWO的MPPT算法在静态与动态环境下的性能指标可知,INBOA的MPPT算法具有更高追踪效率、更快收敛速度以及更小的搜索震荡。从而进一步验证混合算法的优越性。展开更多
光伏阵列在局部遮荫的情况下,其输出特性呈现多峰值,在最大功率跟踪(maximum power point tracking,MPPT)过程中跟踪速度较慢,且跟踪精度不足。针对上述问题提出一种基于邻域搜索策略的蝴蝶优化算法(Butterfly Optimization Algorithm,B...光伏阵列在局部遮荫的情况下,其输出特性呈现多峰值,在最大功率跟踪(maximum power point tracking,MPPT)过程中跟踪速度较慢,且跟踪精度不足。针对上述问题提出一种基于邻域搜索策略的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)和变步长扰动观察法(Perturbation Observation,PO)的融合改进算法。利用改进蝴蝶优化算法的全局搜索能力跟踪到最大功率点附近,通过变步长扰动观察法的局部搜索能力弥补前置算法跟踪精度不足的问题,使其快速准确的跟踪到最大功率点。光伏发电具有随机性和波动性,在应用中常与储能元件相结合,构成光储系统。为了结合实际应用,在MATLAB/Simulink中搭建光储系统模型分析,验证了上述算法在跟踪速度和精度上的优越性。展开更多
文摘Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.
基金This work was supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2016R1C1B1014069)in part by the National Research Foundation of Korea(NRF)funded by the Korea government(MIST)(No.2021R1A2C1013150).
文摘Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.
基金funded by the Deanship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number:IFP22UQU4281768DSR145.
文摘Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.
文摘针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。
文摘在局部遮荫下,针对传统最大功率跟踪MPPT(maximum power point tracking)算法不能跳出局部最优找到全局最大功率,及传统蝴蝶优化算法BOA(butterfly optimization algorithm)存在搜索震荡大和收敛慢等问题,提出一种新型的MPPT控制算法。该算法在传统蝴蝶算法上加入收敛因子,来加快全局搜索速度;引入自适应权重系数,来提高蝴蝶优化算法在局部搜索的搜索速度及追踪精度等性能。通过仿真,对比混合算法(INBOA)与BOA、粒子群优化PSO(particle swarm optimization)算法、灰狼优化算法GWO(gray wolf optimization)的函数收敛曲线,验证所提算法具有收敛速度快、搜索精度高的优点;对比INBOA、BOA、PSO、GWO的MPPT算法在静态与动态环境下的性能指标可知,INBOA的MPPT算法具有更高追踪效率、更快收敛速度以及更小的搜索震荡。从而进一步验证混合算法的优越性。
文摘光伏阵列在局部遮荫的情况下,其输出特性呈现多峰值,在最大功率跟踪(maximum power point tracking,MPPT)过程中跟踪速度较慢,且跟踪精度不足。针对上述问题提出一种基于邻域搜索策略的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)和变步长扰动观察法(Perturbation Observation,PO)的融合改进算法。利用改进蝴蝶优化算法的全局搜索能力跟踪到最大功率点附近,通过变步长扰动观察法的局部搜索能力弥补前置算法跟踪精度不足的问题,使其快速准确的跟踪到最大功率点。光伏发电具有随机性和波动性,在应用中常与储能元件相结合,构成光储系统。为了结合实际应用,在MATLAB/Simulink中搭建光储系统模型分析,验证了上述算法在跟踪速度和精度上的优越性。
文摘针对蝴蝶优化算法(butterfly optimization algorithm,BOA)在复杂环境路径规划过程中求解最短路径时存在收敛速度慢、易陷入局部最优等缺点,提出一种改进的蝴蝶优化算法。首先,在初始化蝴蝶种群时,为保证初代种群多样化,避免陷入局部最优解,通过Tent映射生成初代种群位置;其次,在蝴蝶香味计算阶段引入动态感觉模态,随着迭代过程的持续推进逐步增强蝴蝶的香味值,以缩短收敛时间;再次,为进一步缩短收敛时间,在全局搜索阶段引入遗传算法中的选择因子加快蝴蝶在全局搜索时向最优蝴蝶移动的速度;然后,在局部搜索阶段引入动态变异因子,有效避免在路径规划时陷入局部最优;最后,使用一种基于视线(line of sight,LOS)检测方法的初始种群生成策略,以进一步减少路径中断点的生成,同时确保由BOA算法生成的路径可行解的多样性。实验结果表明,改进的蝴蝶优化算法具有较快的收敛速度,且规划出来的路径在保证路径长度合理的情况下具有更高的平滑度。
